# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask

# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
%matplotlib inline
warnings.filterwarnings("ignore")

nltk.download('all')

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[nltk_data]    | Downloading package porter_test to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package porter_test is already up-to-date!
[nltk_data]    | Downloading package wmt15_eval to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package wmt15_eval is already up-to-date!
[nltk_data]    | Downloading package mwa_ppdb to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package mwa_ppdb is already up-to-date!
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[nltk_data]  Done downloading collection all

True

# path = '/content/drive/MyDrive/Files/'

path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
 
df_tvshows = pd.read_csv(path + 'otttvshows.csv')
 
df_tvshows.head()

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	Language	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type
0	1	Snowpiercer	2013	18+	6.9	94%	NaN	Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso...	Action,Drama,Sci-Fi,Thriller	United States	English	Set seven years after the world has become a f...	60.0	tv series	3.0	1	0	0	0	1
1	2	Philadelphia	1993	13+	8.8	80%	NaN	Charlie Day,Glenn Howerton,Rob McElhenney,Kait...	Comedy	United States	English	The gang, 5 raging alcoholic, narcissists run ...	22.0	tv series	18.0	1	0	0	0	1
2	3	Roma	2018	18+	8.7	93%	NaN	Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ...	Action,Drama,History,Romance,War	United Kingdom,United States	English	In this British historical drama, the turbulen...	52.0	tv series	2.0	1	0	0	0	1
3	4	Amy	2015	18+	7.0	87%	NaN	Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma...	Drama	United States	English	A family drama focused on three generations of...	60.0	tv series	6.0	1	0	1	1	1
4	5	The Young Offenders	2016	NaN	8.0	100%	NaN	Alex Murphy,Chris Walley,Hilary Rose,Dominic M...	Comedy	United Kingdom,Ireland	English	NaN	30.0	tv series	3.0	1	0	0	0	1

# profile = ProfileReport(df_tvshows)
# profile

def data_investigate(df):
    print('No of Rows : ', df.shape[0])
    print('No of Coloums : ', df.shape[1])
    print('**'*25)
    print('Colums Names : \n', df.columns)
    print('**'*25)
    print('Datatype of Columns : \n', df.dtypes)
    print('**'*25)
    print('Missing Values : ')
    c = df.isnull().sum()
    c = c[c > 0]
    print(c)
    print('**'*25)
    print('Missing vaules %age wise :\n')
    print((100*(df.isnull().sum()/len(df.index))))
    print('**'*25)
    print('Pictorial Representation : ')
    plt.figure(figsize = (10, 10))
    sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
    plt.show()

data_investigate(df_tvshows)

No of Rows :  5432
No of Coloums :  20
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb               float64
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime            float64
Kind                object
Seasons            float64
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
dtype: object
**************************************************
Missing Values : 
Age                1954
IMDb                556
Rotten Tomatoes    4194
Directors          5158
Cast                486
Genres              323
Country             549
Language            638
Plotline           2493
Runtime            1410
Seasons             679
dtype: int64
**************************************************
Missing vaules %age wise :

ID                  0.000000
Title               0.000000
Year                0.000000
Age                35.972018
IMDb               10.235641
Rotten Tomatoes    77.209131
Directors          94.955817
Cast                8.946981
Genres              5.946244
Country            10.106775
Language           11.745214
Plotline           45.894698
Runtime            25.957290
Kind                0.000000
Seasons            12.500000
Netflix             0.000000
Hulu                0.000000
Prime Video         0.000000
Disney+             0.000000
Type                0.000000
dtype: float64
**************************************************
Pictorial Representation : 

# ID
# df_tvshows = df_tvshows.drop(['ID'], axis = 1)
 
# Age
df_tvshows.loc[df_tvshows['Age'].isnull() & df_tvshows['Disney+'] == 1, "Age"] = '13'
# df_tvshows.fillna({'Age' : 18}, inplace = True)
df_tvshows.fillna({'Age' : 'NR'}, inplace = True)
df_tvshows['Age'].replace({'all': '0'}, inplace = True)
df_tvshows['Age'].replace({'7+': '7'}, inplace = True)
df_tvshows['Age'].replace({'13+': '13'}, inplace = True)
df_tvshows['Age'].replace({'16+': '16'}, inplace = True)
df_tvshows['Age'].replace({'18+': '18'}, inplace = True)
# df_tvshows['Age'] = df_tvshows['Age'].astype(int)
 
# IMDb
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].mean()}, inplace = True)
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].median()}, inplace = True)
df_tvshows.fillna({'IMDb' : "NA"}, inplace = True)
 
# Rotten Tomatoes
df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].astype(int)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].mean()}, inplace = True)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].median()}, inplace = True)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'].astype(int)
df_tvshows.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
 
# Directors
# df_tvshows = df_tvshows.drop(['Directors'], axis = 1)
df_tvshows.fillna({'Directors' : "NA"}, inplace = True)
 
# Cast
df_tvshows.fillna({'Cast' : "NA"}, inplace = True)
 
# Genres
df_tvshows.fillna({'Genres': "NA"}, inplace = True)
 
# Country
df_tvshows.fillna({'Country': "NA"}, inplace = True)
 
# Language
df_tvshows.fillna({'Language': "NA"}, inplace = True)
 
# Plotline
df_tvshows.fillna({'Plotline': "NA"}, inplace = True)
 
# Runtime
# df_tvshows.fillna({'Runtime' : df_tvshows['Runtime'].mean()}, inplace = True)
# df_tvshows['Runtime'] = df_tvshows['Runtime'].astype(int)
df_tvshows.fillna({'Runtime' : "NA"}, inplace = True)
 
# Kind
# df_tvshows.fillna({'Kind': "NA"}, inplace = True)
 
# Type
# df_tvshows.fillna({'Type': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Type'], axis = 1)
 
# Seasons
# df_tvshows.fillna({'Seasons': 1}, inplace = True)
df_tvshows.fillna({'Seasons': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Seasons'], axis = 1)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
# df_tvshows.fillna({'Seasons' : df_tvshows['Seasons'].mean()}, inplace = True)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
 
# Service Provider
df_tvshows['Service Provider'] = df_tvshows.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_tvshows.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)

# Removing Duplicate and Missing Entries
df_tvshows.dropna(how = 'any', inplace = True)
df_tvshows.drop_duplicates(inplace = True)

data_investigate(df_tvshows)

No of Rows :  5432
No of Coloums :  21
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
       'Service Provider'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb                object
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime             object
Kind                object
Seasons             object
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
Service Provider    object
dtype: object
**************************************************
Missing Values : 
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :

ID                  0.0
Title               0.0
Year                0.0
Age                 0.0
IMDb                0.0
Rotten Tomatoes     0.0
Directors           0.0
Cast                0.0
Genres              0.0
Country             0.0
Language            0.0
Plotline            0.0
Runtime             0.0
Kind                0.0
Seasons             0.0
Netflix             0.0
Hulu                0.0
Prime Video         0.0
Disney+             0.0
Type                0.0
Service Provider    0.0
dtype: float64
**************************************************
Pictorial Representation : 

df_tvshows.head()

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	1	Snowpiercer	2013	18	6.9	94	NA	Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso...	Action,Drama,Sci-Fi,Thriller	United States	...	Set seven years after the world has become a f...	60	tv series	3	1	0	0	0	1	Netflix
1	2	Philadelphia	1993	13	8.8	80	NA	Charlie Day,Glenn Howerton,Rob McElhenney,Kait...	Comedy	United States	...	The gang, 5 raging alcoholic, narcissists run ...	22	tv series	18	1	0	0	0	1	Netflix
2	3	Roma	2018	18	8.7	93	NA	Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ...	Action,Drama,History,Romance,War	United Kingdom,United States	...	In this British historical drama, the turbulen...	52	tv series	2	1	0	0	0	1	Netflix
3	4	Amy	2015	18	7	87	NA	Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma...	Drama	United States	...	A family drama focused on three generations of...	60	tv series	6	1	0	1	1	1	Netflix
4	5	The Young Offenders	2016	NR	8	100	NA	Alex Murphy,Chris Walley,Hilary Rose,Dominic M...	Comedy	United Kingdom,Ireland	...	NA	30	tv series	3	1	0	0	0	1	Netflix

5 rows × 21 columns

df_tvshows.describe()

	ID	Year	Netflix	Hulu	Prime Video	Disney+	Type
count	5432.000000	5432.000000	5432.000000	5432.000000	5432.000000	5432.000000	5432.0
mean	2716.500000	2010.668446	0.341311	0.293999	0.403351	0.033689	1.0
std	1568.227662	11.726176	0.474193	0.455633	0.490615	0.180445	0.0
min	1.000000	1901.000000	0.000000	0.000000	0.000000	0.000000	1.0
25%	1358.750000	2009.000000	0.000000	0.000000	0.000000	0.000000	1.0
50%	2716.500000	2014.000000	0.000000	0.000000	0.000000	0.000000	1.0
75%	4074.250000	2017.000000	1.000000	1.000000	1.000000	0.000000	1.0
max	5432.000000	2020.000000	1.000000	1.000000	1.000000	1.000000	1.0

df_tvshows.corr()

	ID	Year	Netflix	Hulu	Prime Video	Disney+	Type
ID	1.000000	-0.031346	-0.646330	0.034293	0.441264	0.195409	NaN
Year	-0.031346	1.000000	0.222316	-0.065807	-0.198675	-0.022741	NaN
Netflix	-0.646330	0.222316	1.000000	-0.366515	-0.515086	-0.119344	NaN
Hulu	0.034293	-0.065807	-0.366515	1.000000	-0.377374	-0.075701	NaN
Prime Video	0.441264	-0.198675	-0.515086	-0.377374	1.000000	-0.151442	NaN
Disney+	0.195409	-0.022741	-0.119344	-0.075701	-0.151442	1.000000	NaN
Type	NaN	NaN	NaN	NaN	NaN	NaN	NaN

# df_tvshows.sort_values('Year', ascending = True)
# df_tvshows.sort_values('IMDb', ascending = False)

# df_tvshows.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_otttvshows.csv', index = False)
 
# path = '/content/drive/MyDrive/Files/'
 
# udf_tvshows = pd.read_csv(path + 'updated_otttvshows.csv')
 
# udf_tvshows

# df_netflix_tvshows = df_tvshows.loc[(df_tvshows['Netflix'] > 0)]
# df_hulu_tvshows = df_tvshows.loc[(df_tvshows['Hulu'] > 0)]
# df_prime_video_tvshows = df_tvshows.loc[(df_tvshows['Prime Video'] > 0)]
# df_disney_tvshows = df_tvshows.loc[(df_tvshows['Disney+'] > 0)]

df_netflix_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 1) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_hulu_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 1) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_prime_video_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 1 ) & (df_tvshows['Disney+'] == 0)]
df_disney_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 1)]

df_tvshows_imdb = df_tvshows.copy()

df_tvshows_imdb.drop(df_tvshows_imdb.loc[df_tvshows_imdb['IMDb'] == "NA"].index, inplace = True)
# df_tvshows_imdb = df_tvshows_imdb[df_tvshows_imdb.IMDb != "NA"]
df_tvshows_imdb['IMDb'] = df_tvshows_imdb['IMDb'].astype(int)

# Creating distinct dataframes only with the tvshows present on individual streaming platforms
netflix_imdb_tvshows = df_tvshows_imdb.loc[df_tvshows_imdb['Netflix'] == 1]
hulu_imdb_tvshows = df_tvshows_imdb.loc[df_tvshows_imdb['Hulu'] == 1]
prime_video_imdb_tvshows = df_tvshows_imdb.loc[df_tvshows_imdb['Prime Video'] == 1]
disney_imdb_tvshows = df_tvshows_imdb.loc[df_tvshows_imdb['Disney+'] == 1]

df_tvshows_imdb_group = df_tvshows_imdb.copy()

plt.figure(figsize = (10, 10))
corr = df_tvshows_imdb.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, allow annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()

<Figure size 720x720 with 0 Axes>

df_imdb_high_tvshows = df_tvshows_imdb.sort_values(by = 'IMDb', ascending = False).reset_index()
df_imdb_high_tvshows = df_imdb_high_tvshows.drop(['index'], axis = 1)
# filter = (df_tvshows_imdb['IMDb'] == (df_tvshows_imdb['IMDb'].max()))
# df_imdb_high_tvshows = df_tvshows_imdb[filter]
 
# highest_rated_tvshows = df_tvshows_imdb.loc[df_tvshows_imdb['IMDb'].idxmax()]
 
print('\nTV Shows with Highest Ever IMDb  are : \n')
df_imdb_high_tvshows.head(5)

TV Shows with Highest Ever IMDb  are : 

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	5129	Tanks!	2005	7	10	NA	NA	David Fletcher,Graham McTavish,John Erickson,B...	History,War	United States	...	In this series Tom Hubbard travels throughout ...	NA	tv series	1	0	0	1	0	1	Prime Video
1	3718	The Sopranos	1999	18	9	92	NA	James Gandolfini,Edie Falco,Michael Imperioli,...	Crime,Drama	United States	...	Harry Bosch is an irreverent homicide detectiv...	55	tv series	6	0	0	1	0	1	Prime Video
2	144	The Chosen	1981	7	9	75	NA	Shahar Isaac,Jonathan Roumie,Noah James,Paras ...	Drama,History	United States	...	Depressed after the passing of his father (Dha...	54	tv series	2	0	0	1	0	1	Prime Video
3	835	Africa	2013	7	9	NA	NA	David Attenborough,Simon Blakeney,James Aldred...	Documentary	United Kingdom	...	Three ordinary teenage girls discover a moon p...	360	tv series	1	1	0	0	0	1	Netflix
4	5304	The Imagineering Story	2019	0	9	100	NA	Tom Morris,Kevin Rafferty,Angela Bassett,Tom F...	Documentary	United States	...	Raven Baxter is a teenager. She can see glimps...	60	tv series	1	0	0	0	1	1	Disney+

5 rows × 21 columns

fig = px.bar(y = df_imdb_high_tvshows['Title'][:15],
             x = df_imdb_high_tvshows['IMDb'][:15], 
             color = df_imdb_high_tvshows['IMDb'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Highest IMDb : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

df_imdb_low_tvshows = df_tvshows_imdb.sort_values(by = 'IMDb', ascending = True).reset_index()
df_imdb_low_tvshows = df_imdb_low_tvshows.drop(['index'], axis = 1)
# filter = (df_tvshows_imdb['IMDb'] == (df_tvshows_imdb['IMDb'].min()))
# df_imdb_low_tvshows = df_tvshows_imdb[filter]

print('\nTV Shows with Lowest Ever IMDb  are : \n')
df_imdb_low_tvshows.head(5)

TV Shows with Lowest Ever IMDb  are : 

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	5245	PINKFONG!	2017	NR	1	NA	NA	Marie Segoine	Family	NA	...	NA	NA	tv series	1	0	0	1	0	1	Prime Video
1	3368	My Super Sweet 16	2005	7	1	NA	NA	Mary Morrison,Quincy Brown,Shad Moss,Cher Hubs...	Documentary,Reality-TV	United States	...	Geologist Martin Pepper and Biologist Liz Bonn...	30	tv series	10	0	1	0	0	1	Hulu
2	3336	Toddlers & Tiaras	2009	7	1	NA	NA	MaKenzie Myers,Dawn Rochelle,Wendy D. Lee,Alan...	Reality-TV	United States	...	NA	NA	tv series	7	0	1	1	0	1	Prime Video
3	4820	Pinkfong! Baby Shark Special	2017	NR	1	NA	NA	Marie Segoine	Family	NA	...	The Bumble Nums are back with all new adventur...	NA	tv series	1	0	0	1	0	1	Prime Video
4	338	12-12-12	2012	18	1	NA	NA	Colin Murray,John Hartson,Robbie Savage	Sport	NA	...	Hugo (Gabriel Byrne), heir to a fortune, is ma...	NA	tv series	NA	0	0	1	0	1	Prime Video

5 rows × 21 columns

fig = px.bar(y = df_imdb_low_tvshows['Title'][:15],
             x = df_imdb_low_tvshows['IMDb'][:15], 
             color = df_imdb_low_tvshows['IMDb'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Lowest IMDb : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

print(f'''
      Total '{df_tvshows_imdb['IMDb'].unique().shape[0]}' unique IMDb s were Given, They were Like this,\n
      
{df_tvshows_imdb.sort_values(by = 'IMDb', ascending = False)['IMDb'].unique()}\n
 
      The Highest Ever IMDb Ever Any TV Show Got is '{df_imdb_high_tvshows['Title'][0]}' : '{df_imdb_high_tvshows['IMDb'].max()}'\n
 
      The Lowest Ever IMDb Ever Any TV Show Got is '{df_imdb_low_tvshows['Title'][0]}' : '{df_imdb_low_tvshows['IMDb'].min()}'\n
      ''')

      Total '10' unique IMDb s were Given, They were Like this,

      
[10  9  8  7  6  5  4  3  2  1]

 
      The Highest Ever IMDb Ever Any TV Show Got is 'Tanks!' : '10'

 
      The Lowest Ever IMDb Ever Any TV Show Got is 'PINKFONG!' : '1'

      

netflix_imdb_high_tvshows = df_imdb_high_tvshows.loc[df_imdb_high_tvshows['Netflix']==1].reset_index()
netflix_imdb_high_tvshows = netflix_imdb_high_tvshows.drop(['index'], axis = 1)
 
netflix_imdb_low_tvshows = df_imdb_low_tvshows.loc[df_imdb_low_tvshows['Netflix']==1].reset_index()
netflix_imdb_low_tvshows = netflix_imdb_low_tvshows.drop(['index'], axis = 1)
 
netflix_imdb_high_tvshows.head(5)

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	835	Africa	2013	7	9	NA	NA	David Attenborough,Simon Blakeney,James Aldred...	Documentary	United Kingdom	...	Three ordinary teenage girls discover a moon p...	360	tv series	1	1	0	0	0	1	Netflix
1	779	Yeh Meri Family	2018	16	9	NA	NA	Vishesh Bansal,Mona Singh,Akarsh Khurana,Ahan ...	Comedy,Drama,Family,Musical,Romance	India	...	Three teen strangers wake up in a mysterious l...	30	tv series	1	1	0	0	0	1	Netflix
2	1576	Raja Rasoi Aur Anya Kahaniyan	2015	NR	9	NA	NA	Manwendra Tripathy	History	India	...	Amamiya Shuuhei moves from Tokyo to the countr...	45	tv series	2	1	0	1	0	1	Netflix
3	1212	Ch:os:en	2013	7	9	NA	NA	Shahar Isaac,Jonathan Roumie,Noah James,Paras ...	Drama,History	United States	...	A madly intense whirlwind drama about love, be...	54	tv series	2	1	0	0	0	1	Netflix
4	505	Breaking Bad	2008	16	9	96	NA	Bryan Cranston,Anna Gunn,Aaron Paul,Betsy Bran...	Crime,Drama,Thriller	United States	...	When chemistry teacher Walter White is diagnos...	49	tv series	5	1	0	0	0	1	Netflix

5 rows × 21 columns

fig = px.bar(y = netflix_imdb_high_tvshows['Title'][:15],
             x = netflix_imdb_high_tvshows['IMDb'][:15], 
             color = netflix_imdb_high_tvshows['IMDb'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Highest IMDb : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

fig = px.bar(y = netflix_imdb_low_tvshows['Title'][:15],
             x = netflix_imdb_low_tvshows['IMDb'][:15], 
             color = netflix_imdb_low_tvshows['IMDb'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Lowest IMDb : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

hulu_imdb_high_tvshows = df_imdb_high_tvshows.loc[df_imdb_high_tvshows['Hulu']==1].reset_index()
hulu_imdb_high_tvshows = hulu_imdb_high_tvshows.drop(['index'], axis = 1)
 
hulu_imdb_low_tvshows = df_imdb_low_tvshows.loc[df_imdb_low_tvshows['Hulu']==1].reset_index()
hulu_imdb_low_tvshows = hulu_imdb_low_tvshows.drop(['index'], axis = 1)
 
hulu_imdb_high_tvshows.head(5)

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	3395	Hungry Henry	2014	NR	9	NA	NA	NA	NA	United States	...	NA	11	tv series	2	0	1	0	0	1	Hulu
1	2694	Land of Honor	2014	16	9	NA	NA	Jun Gong,Zhehan Zhang,Ye Zhou,Gong Jun,Ma Wen ...	Action,Drama,Fantasy,History	China	...	The Haves and the Have Nots is a primetime cab...	NA	tv series	1	0	1	0	0	1	Hulu
2	589	Death Note	2006	18	9	100	NA	Mamoru Miyano,Brad Swaile,Vincent Tong,Ryô Nai...	Animation,Crime,Drama,Fantasy,Mystery,Thriller	Japan	...	On Nadia's 36th birthday she is struck by a ca...	24	tv series	1	1	1	0	0	1	Netflix
3	2664	The Joy of Painting	1983	0	9	NA	NA	Bob Ross,Steve Ross,Dana Jester,Peep,John Tham...	Documentary,Family	United States	...	The Winslow family is a pretty normal family e...	30	tv series	31	0	1	1	0	1	Prime Video
4	3606	The Adventures of Dr. Buckeye Bottoms	2017	NR	9	NA	NA	Bartholomew Buckeye Bottoms,Zac Fine	Reality-TV	United States	...	NA	NA	tv series	2	0	1	0	0	1	Hulu

5 rows × 21 columns

fig = px.bar(y = hulu_imdb_high_tvshows['Title'][:15],
             x = hulu_imdb_high_tvshows['IMDb'][:15], 
             color = hulu_imdb_high_tvshows['IMDb'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Highest IMDb : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

fig = px.bar(y = hulu_imdb_low_tvshows['Title'][:15],
             x = hulu_imdb_low_tvshows['IMDb'][:15], 
             color = hulu_imdb_low_tvshows['IMDb'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Lowest IMDb : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

prime_video_imdb_high_tvshows = df_imdb_high_tvshows.loc[df_imdb_high_tvshows['Prime Video']==1].reset_index()
prime_video_imdb_high_tvshows = prime_video_imdb_high_tvshows.drop(['index'], axis = 1)
 
prime_video_imdb_low_tvshows = df_imdb_low_tvshows.loc[df_imdb_low_tvshows['Prime Video']==1].reset_index()
prime_video_imdb_low_tvshows = prime_video_imdb_low_tvshows.drop(['index'], axis = 1)
 
prime_video_imdb_high_tvshows.head(5)

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	5129	Tanks!	2005	7	10	NA	NA	David Fletcher,Graham McTavish,John Erickson,B...	History,War	United States	...	In this series Tom Hubbard travels throughout ...	NA	tv series	1	0	0	1	0	1	Prime Video
1	3718	The Sopranos	1999	18	9	92	NA	James Gandolfini,Edie Falco,Michael Imperioli,...	Crime,Drama	United States	...	Harry Bosch is an irreverent homicide detectiv...	55	tv series	6	0	0	1	0	1	Prime Video
2	144	The Chosen	1981	7	9	75	NA	Shahar Isaac,Jonathan Roumie,Noah James,Paras ...	Drama,History	United States	...	Depressed after the passing of his father (Dha...	54	tv series	2	0	0	1	0	1	Prime Video
3	4124	Harmony with A R Rahman	2018	NR	9	NA	NA	A.R. Rahman,Ustad Mohi Baha'uddin Dagar,Sajith...	Documentary,Musical	India	...	Our thirties and forties are the busiest time ...	NA	tv series	1	0	0	1	0	1	Prime Video
4	1576	Raja Rasoi Aur Anya Kahaniyan	2015	NR	9	NA	NA	Manwendra Tripathy	History	India	...	Amamiya Shuuhei moves from Tokyo to the countr...	45	tv series	2	1	0	1	0	1	Netflix

5 rows × 21 columns

fig = px.bar(y = prime_video_imdb_high_tvshows['Title'][:15],
             x = prime_video_imdb_high_tvshows['IMDb'][:15], 
             color = prime_video_imdb_high_tvshows['IMDb'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Highest IMDb : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

fig = px.bar(y = prime_video_imdb_low_tvshows['Title'][:15],
             x = prime_video_imdb_low_tvshows['IMDb'][:15], 
             color = prime_video_imdb_low_tvshows['IMDb'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Lowest IMDb : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

disney_imdb_high_tvshows = df_imdb_high_tvshows.loc[df_imdb_high_tvshows['Disney+']==1].reset_index()
disney_imdb_high_tvshows = disney_imdb_high_tvshows.drop(['index'], axis = 1)
 
disney_imdb_low_tvshows = df_imdb_low_tvshows.loc[df_imdb_low_tvshows['Disney+']==1].reset_index()
disney_imdb_low_tvshows = disney_imdb_low_tvshows.drop(['index'], axis = 1)
 
disney_imdb_high_tvshows.head(5)

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	5304	The Imagineering Story	2019	0	9	100	NA	Tom Morris,Kevin Rafferty,Angela Bassett,Tom F...	Documentary	United States	...	Raven Baxter is a teenager. She can see glimps...	60	tv series	1	0	0	0	1	1	Disney+
1	493	The Other Me	2000	0	9	NA	Sotiris Tsafoulias	Pigmalion Dadakaridis,Petros Lagoutis,Vicky Pa...	Crime,Drama,Mystery,Thriller	Greece	...	NA	45	tv series	2	0	0	0	1	1	Disney+
2	483	Avatar	2009	13	9	82	NA	Dee Bradley Baker,Zach Tyler,Mae Whitman,Jack ...	Animation,Action,Adventure,Family,Fantasy,Mystery	United States	...	In a suburban fantasy world, two teenage elf b...	23	tv series	3	0	0	0	1	1	Disney+
3	1037	Brain Games	2011	0	8	NA	NA	Jason Silva,Bert Thomas Morris,Apollo Robbins,...	Documentary,Comedy,Drama,Game-Show,Reality-TV	United States	...	NA	60	tv series	6	1	0	0	1	1	Netflix
4	2490	Star vs. the Forces of Evil	2015	7	8	NA	NA	Eden Sher,Adam McArthur,Grey Griffin,Daron Nef...	Animation,Action,Adventure,Comedy,Drama,Family...	United States,Spain,United Kingdom,Mexico,Japan	...	Set in 2008 and against the hugely atmospheric...	22	tv series	4	0	1	0	1	1	Disney+

5 rows × 21 columns

fig = px.bar(y = disney_imdb_high_tvshows['Title'][:15],
             x = disney_imdb_high_tvshows['IMDb'][:15], 
             color = disney_imdb_high_tvshows['IMDb'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Highest IMDb : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

fig = px.bar(y = disney_imdb_low_tvshows['Title'][:15],
             x = disney_imdb_low_tvshows['IMDb'][:15], 
             color = disney_imdb_low_tvshows['IMDb'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Lowest IMDb : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

print(f'''
      The TV Show with Highest IMDb  Ever Got is '{df_imdb_high_tvshows['Title'][0]}' : '{df_imdb_high_tvshows['IMDb'].max()}'\n
      The TV Show with Lowest IMDb  Ever Got is '{df_imdb_low_tvshows['Title'][0]}' : '{df_imdb_low_tvshows['IMDb'].min()}'\n
      
      The TV Show with Highest IMDb  on 'Netflix' is '{netflix_imdb_high_tvshows['Title'][0]}' : '{netflix_imdb_high_tvshows['IMDb'].max()}'\n
      The TV Show with Lowest IMDb  on 'Netflix' is '{netflix_imdb_low_tvshows['Title'][0]}' : '{netflix_imdb_low_tvshows['IMDb'].min()}'\n
      
      The TV Show with Highest IMDb  on 'Hulu' is '{hulu_imdb_high_tvshows['Title'][0]}' : '{hulu_imdb_high_tvshows['IMDb'].max()}'\n
      The TV Show with Lowest IMDb  on 'Hulu' is '{hulu_imdb_low_tvshows['Title'][0]}' : '{hulu_imdb_low_tvshows['IMDb'].min()}'\n
      
      The TV Show with Highest IMDb  on 'Prime Video' is '{prime_video_imdb_high_tvshows['Title'][0]}' : '{prime_video_imdb_high_tvshows['IMDb'].max()}'\n
      The TV Show with Lowest IMDb  on 'Prime Video' is '{prime_video_imdb_low_tvshows['Title'][0]}' : '{prime_video_imdb_low_tvshows['IMDb'].min()}'\n
      
      The TV Show with Highest IMDb  on 'Disney+' is '{disney_imdb_high_tvshows['Title'][0]}' : '{disney_imdb_high_tvshows['IMDb'].max()}'\n
      The TV Show with Lowest IMDb  on 'Disney+' is '{disney_imdb_low_tvshows['Title'][0]}' : '{disney_imdb_low_tvshows['IMDb'].min()}'\n 
      ''')

      The TV Show with Highest IMDb  Ever Got is 'Tanks!' : '10'

      The TV Show with Lowest IMDb  Ever Got is 'PINKFONG!' : '1'

      
      The TV Show with Highest IMDb  on 'Netflix' is 'Africa' : '9'

      The TV Show with Lowest IMDb  on 'Netflix' is 'Game Winning Hit' : '2'

      
      The TV Show with Highest IMDb  on 'Hulu' is 'Hungry Henry' : '9'

      The TV Show with Lowest IMDb  on 'Hulu' is 'My Super Sweet 16' : '1'

      
      The TV Show with Highest IMDb  on 'Prime Video' is 'Tanks!' : '10'

      The TV Show with Lowest IMDb  on 'Prime Video' is 'PINKFONG!' : '1'

      
      The TV Show with Highest IMDb  on 'Disney+' is 'The Imagineering Story' : '9'

      The TV Show with Lowest IMDb  on 'Disney+' is 'Bizaardvark' : '3'
 
      

print(f'''
      Accross All Platforms the Average IMDb  is '{round(df_tvshows_imdb['IMDb'].mean(), ndigits = 2)}'\n
      The Average IMDb  on 'Netflix' is '{round(netflix_imdb_tvshows['IMDb'].mean(), ndigits = 2)}'\n
      The Average IMDb  on 'Hulu' is '{round(hulu_imdb_tvshows['IMDb'].mean(), ndigits = 2)}'\n
      The Average IMDb  on 'Prime Video' is '{round(prime_video_imdb_tvshows['IMDb'].mean(), ndigits = 2)}'\n
      The Average IMDb  on 'Disney+' is '{round(disney_imdb_tvshows['IMDb'].mean(), ndigits = 2)}'\n 
      ''')

      Accross All Platforms the Average IMDb  is '6.7'

      The Average IMDb  on 'Netflix' is '6.77'

      The Average IMDb  on 'Hulu' is '6.64'

      The Average IMDb  on 'Prime Video' is '6.71'

      The Average IMDb  on 'Disney+' is '6.58'
 
      

f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(df_tvshows_imdb['IMDb'],bins = 20, kde = True, ax = ax[0])
sns.boxplot(df_tvshows_imdb['IMDb'], ax = ax[1])
plt.show()

# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('IMDb s Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_imdb_tvshows['IMDb'][:100], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_imdb_tvshows['IMDb'][:100], color = 'red', legend = True, kde = True)
sns.histplot(hulu_imdb_tvshows['IMDb'][:100], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_imdb_tvshows['IMDb'][:100], color = 'darkblue', legend = True, kde = True) 
 
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()

def round_val(data):
    if str(data) != 'nan':
        return round(data)

df_tvshows_imdb_group['IMDb Group'] = df_tvshows_imdb['IMDb'].apply(round_val)
 
imdb_values = df_tvshows_imdb_group['IMDb Group'].value_counts().sort_index(ascending = False).tolist()
imdb_index = df_tvshows_imdb_group['IMDb Group'].value_counts().sort_index(ascending = False).index
 
# imdb_values, imdb_index

imdb_group_count = df_tvshows_imdb_group.groupby('IMDb Group')['Title'].count()
imdb_group_tvshows = df_tvshows_imdb_group.groupby('IMDb Group')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
imdb_group_data_tvshows = pd.concat([imdb_group_count, imdb_group_tvshows], axis = 1).reset_index().rename(columns = {'Title' : 'TV Shows Count'})
imdb_group_data_tvshows = imdb_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)

# IMDb Group with TV Shows Counts - All Platforms Combined
imdb_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)

	IMDb Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
6	7	1956	737	603	694	53
7	8	1147	456	339	432	41
5	6	1065	415	316	349	55
4	5	404	139	119	156	22
3	4	163	45	57	62	7
2	3	61	17	25	23	1
8	9	53	18	12	27	3
1	2	19	4	10	6	0
0	1	7	0	3	5	0
9	10	1	0	0	1	0

imdb_group_data_tvshows.sort_values(by = 'IMDb Group', ascending = False)

	IMDb Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
9	10	1	0	0	1	0
8	9	53	18	12	27	3
7	8	1147	456	339	432	41
6	7	1956	737	603	694	53
5	6	1065	415	316	349	55
4	5	404	139	119	156	22
3	4	163	45	57	62	7
2	3	61	17	25	23	1
1	2	19	4	10	6	0
0	1	7	0	3	5	0

fig = px.bar(y = imdb_group_data_tvshows['TV Shows Count'],
             x = imdb_group_data_tvshows['IMDb Group'], 
             color = imdb_group_data_tvshows['IMDb Group'],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows Count', 'x' : 'IMDb : Rating'},
             title  = 'TV Shows with Group IMDb : All Platforms')

fig.update_layout(plot_bgcolor = "white")
fig.show()

fig = px.pie(imdb_group_data_tvshows[:10],
             names = imdb_group_data_tvshows['IMDb Group'],
             values = imdb_group_data_tvshows['TV Shows Count'],
             color = imdb_group_data_tvshows['TV Shows Count'],
             color_discrete_sequence = px.colors.sequential.Teal)

fig.update_traces(textinfo = 'percent+label',
                  title = 'TV Shows Count based on IMDb Group')
fig.show()

df_imdb_group_high_tvshows = imdb_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False).reset_index()
df_imdb_group_high_tvshows = df_imdb_group_high_tvshows.drop(['index'], axis = 1)
# filter = (imdb_group_data_tvshows['TV Shows Count'] ==  (imdb_group_data_tvshows['TV Shows Count'].max()))
# df_imdb_group_high_tvshows = imdb_group_data_tvshows[filter]
 
# highest_rated_tvshows = imdb_group_data_tvshows.loc[imdb_group_data_tvshows['TV Shows Count'].idxmax()]
 
# print('\nIMDb with Highest Ever TV Shows Count are : All Platforms Combined\n')
df_imdb_group_high_tvshows.head(5)

	IMDb Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	7	1956	737	603	694	53
1	8	1147	456	339	432	41
2	6	1065	415	316	349	55
3	5	404	139	119	156	22
4	4	163	45	57	62	7

df_imdb_group_low_tvshows = imdb_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = True).reset_index()
df_imdb_group_low_tvshows = df_imdb_group_low_tvshows.drop(['index'], axis = 1)
# filter = (imdb_group_data_tvshows['TV Shows Count'] = =  (imdb_group_data_tvshows['TV Shows Count'].min()))
# df_imdb_group_low_tvshows = imdb_group_data_tvshows[filter]
 
# print('\nIMDb with Lowest Ever TV Shows Count are : All Platforms Combined\n')
df_imdb_group_low_tvshows.head(5)

	IMDb Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	10	1	0	0	1	0
1	1	7	0	3	5	0
2	2	19	4	10	6	0
3	9	53	18	12	27	3
4	3	61	17	25	23	1

print(f'''
      Total '{df_tvshows_imdb['IMDb'].count()}' Titles are available on All Platforms, out of which\n
      You Can Choose to see TV Shows from Total '{imdb_group_data_tvshows['IMDb Group'].unique().shape[0]}' IMDb Group, They were Like this, \n
 
      {imdb_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)['IMDb Group'].unique()} etc. \n
 
      The IMDb Group with Highest TV Shows Count have '{imdb_group_data_tvshows['TV Shows Count'].max()}' TV Shows Available is '{df_imdb_group_high_tvshows['IMDb Group'][0]}', &\n
      The IMDb Group with Lowest TV Shows Count have '{imdb_group_data_tvshows['TV Shows Count'].min()}' TV Shows Available is '{df_imdb_group_low_tvshows['IMDb Group'][0]}'
      ''')

      Total '4876' Titles are available on All Platforms, out of which

      You Can Choose to see TV Shows from Total '10' IMDb Group, They were Like this, 

 
      [ 7  8  6  5  4  3  9  2  1 10] etc. 

 
      The IMDb Group with Highest TV Shows Count have '1956' TV Shows Available is '7', &

      The IMDb Group with Lowest TV Shows Count have '1' TV Shows Available is '10'
      

netflix_imdb_group_tvshows = imdb_group_data_tvshows[imdb_group_data_tvshows['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_imdb_group_tvshows = netflix_imdb_group_tvshows.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
netflix_imdb_group_high_tvshows = df_imdb_group_high_tvshows.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_imdb_group_high_tvshows = netflix_imdb_group_high_tvshows.drop(['index'], axis = 1)
 
netflix_imdb_group_low_tvshows = df_imdb_group_high_tvshows.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_imdb_group_low_tvshows = netflix_imdb_group_low_tvshows.drop(['index'], axis = 1)
 
netflix_imdb_group_high_tvshows.head(5)

	IMDb Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	7	1956	737	603	694	53
1	8	1147	456	339	432	41
2	6	1065	415	316	349	55
3	5	404	139	119	156	22
4	4	163	45	57	62	7

hulu_imdb_group_tvshows = imdb_group_data_tvshows[imdb_group_data_tvshows['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_imdb_group_tvshows = hulu_imdb_group_tvshows.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
hulu_imdb_group_high_tvshows = df_imdb_group_high_tvshows.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_imdb_group_high_tvshows = hulu_imdb_group_high_tvshows.drop(['index'], axis = 1)
 
hulu_imdb_group_low_tvshows = df_imdb_group_high_tvshows.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_imdb_group_low_tvshows = hulu_imdb_group_low_tvshows.drop(['index'], axis = 1)
 
hulu_imdb_group_high_tvshows.head(5)

	IMDb Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	7	1956	737	603	694	53
1	8	1147	456	339	432	41
2	6	1065	415	316	349	55
3	5	404	139	119	156	22
4	4	163	45	57	62	7

prime_video_imdb_group_tvshows = imdb_group_data_tvshows[imdb_group_data_tvshows['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_imdb_group_tvshows = prime_video_imdb_group_tvshows.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'TV Shows Count'], axis = 1)
 
prime_video_imdb_group_high_tvshows = df_imdb_group_high_tvshows.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_imdb_group_high_tvshows = prime_video_imdb_group_high_tvshows.drop(['index'], axis = 1)
 
prime_video_imdb_group_low_tvshows = df_imdb_group_high_tvshows.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_imdb_group_low_tvshows = prime_video_imdb_group_low_tvshows.drop(['index'], axis = 1)
 
prime_video_imdb_group_high_tvshows.head(5)

	IMDb Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	7	1956	737	603	694	53
1	8	1147	456	339	432	41
2	6	1065	415	316	349	55
3	5	404	139	119	156	22
4	4	163	45	57	62	7

disney_imdb_group_tvshows = imdb_group_data_tvshows[imdb_group_data_tvshows['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_imdb_group_tvshows = disney_imdb_group_tvshows.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'TV Shows Count'], axis = 1)
 
disney_imdb_group_high_tvshows = df_imdb_group_high_tvshows.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_imdb_group_high_tvshows = disney_imdb_group_high_tvshows.drop(['index'], axis = 1)
 
disney_imdb_group_low_tvshows = df_imdb_group_high_tvshows.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_imdb_group_low_tvshows = disney_imdb_group_low_tvshows.drop(['index'], axis = 1)
 
disney_imdb_group_high_tvshows.head(5)

	IMDb Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	6	1065	415	316	349	55
1	7	1956	737	603	694	53
2	8	1147	456	339	432	41
3	5	404	139	119	156	22
4	4	163	45	57	62	7

print(f'''
      The IMDb Group with Highest TV Shows Count Ever Got is '{df_imdb_group_high_tvshows['IMDb Group'][0]}' : '{df_imdb_group_high_tvshows['TV Shows Count'].max()}'\n
      The IMDb Group with Lowest TV Shows Count Ever Got is '{df_imdb_group_low_tvshows['IMDb Group'][0]}' : '{df_imdb_group_low_tvshows['TV Shows Count'].min()}'\n
      
      The IMDb Group with Highest TV Shows Count on 'Netflix' is '{netflix_imdb_group_high_tvshows['IMDb Group'][0]}' : '{netflix_imdb_group_high_tvshows['Netflix'].max()}'\n
      The IMDb Group with Lowest TV Shows Count on 'Netflix' is '{netflix_imdb_group_low_tvshows['IMDb Group'][0]}' : '{netflix_imdb_group_low_tvshows['Netflix'].min()}'\n
      
      The IMDb Group with Highest TV Shows Count on 'Hulu' is '{hulu_imdb_group_high_tvshows['IMDb Group'][0]}' : '{hulu_imdb_group_high_tvshows['Hulu'].max()}'\n
      The IMDb Group with Lowest TV Shows Count on 'Hulu' is '{hulu_imdb_group_low_tvshows['IMDb Group'][0]}' : '{hulu_imdb_group_low_tvshows['Hulu'].min()}'\n
      
      The IMDb Group with Highest TV Shows Count on 'Prime Video' is '{prime_video_imdb_group_high_tvshows['IMDb Group'][0]}' : '{prime_video_imdb_group_high_tvshows['Prime Video'].max()}'\n
      The IMDb Group with Lowest TV Shows Count on 'Prime Video' is '{prime_video_imdb_group_low_tvshows['IMDb Group'][0]}' : '{prime_video_imdb_group_low_tvshows['Prime Video'].min()}'\n
      
      The IMDb Group with Highest TV Shows Count on 'Disney+' is '{disney_imdb_group_high_tvshows['IMDb Group'][0]}' : '{disney_imdb_group_high_tvshows['Disney+'].max()}'\n
      The IMDb Group with Lowest TV Shows Count on 'Disney+' is '{disney_imdb_group_low_tvshows['IMDb Group'][0]}' : '{disney_imdb_group_low_tvshows['Disney+'].min()}'\n 
      ''')

      The IMDb Group with Highest TV Shows Count Ever Got is '7' : '1956'

      The IMDb Group with Lowest TV Shows Count Ever Got is '10' : '1'

      
      The IMDb Group with Highest TV Shows Count on 'Netflix' is '7' : '737'

      The IMDb Group with Lowest TV Shows Count on 'Netflix' is '1' : '0'

      
      The IMDb Group with Highest TV Shows Count on 'Hulu' is '7' : '603'

      The IMDb Group with Lowest TV Shows Count on 'Hulu' is '10' : '0'

      
      The IMDb Group with Highest TV Shows Count on 'Prime Video' is '7' : '694'

      The IMDb Group with Lowest TV Shows Count on 'Prime Video' is '10' : '1'

      
      The IMDb Group with Highest TV Shows Count on 'Disney+' is '6' : '55'

      The IMDb Group with Lowest TV Shows Count on 'Disney+' is '2' : '0'
 
      

fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_i_ax1 = sns.barplot(x = netflix_imdb_group_tvshows['IMDb Group'][:10], y = netflix_imdb_group_tvshows['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_i_ax2 = sns.barplot(x = hulu_imdb_group_tvshows['IMDb Group'][:10], y = hulu_imdb_group_tvshows['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_i_ax3 = sns.barplot(x = prime_video_imdb_group_tvshows['IMDb Group'][:10], y = prime_video_imdb_group_tvshows['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_i_ax4 = sns.barplot(x = disney_imdb_group_tvshows['IMDb Group'][:10], y = disney_imdb_group_tvshows['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_i_ax1.title.set_text(labels[0])
h_i_ax2.title.set_text(labels[1])
p_i_ax3.title.set_text(labels[2])
d_i_ax4.title.set_text(labels[3])
 
plt.show()

plt.figure(figsize = (20, 5))
sns.lineplot(x = imdb_group_data_tvshows['IMDb Group'], y = imdb_group_data_tvshows['Netflix'], color = 'red')
sns.lineplot(x = imdb_group_data_tvshows['IMDb Group'], y = imdb_group_data_tvshows['Hulu'], color = 'lightgreen')
sns.lineplot(x = imdb_group_data_tvshows['IMDb Group'], y = imdb_group_data_tvshows['Prime Video'], color = 'lightblue')
sns.lineplot(x = imdb_group_data_tvshows['IMDb Group'], y = imdb_group_data_tvshows['Disney+'], color = 'darkblue')
plt.xlabel('IMDb Group', fontsize = 15)
plt.ylabel('TV Shows Count', fontsize = 15)
plt.show()

print(f'''
      Accross All Platforms Total Count of IMDb Group is '{imdb_group_data_tvshows['IMDb Group'].unique().shape[0]}'\n
      Total Count of IMDb Group on 'Netflix' is '{netflix_imdb_group_tvshows['IMDb Group'].unique().shape[0]}'\n
      Total Count of IMDb Group on 'Hulu' is '{hulu_imdb_group_tvshows['IMDb Group'].unique().shape[0]}'\n
      Total Count of IMDb Group on 'Prime Video' is '{prime_video_imdb_group_tvshows['IMDb Group'].unique().shape[0]}'\n
      Total Count of IMDb Group on 'Disney+' is '{disney_imdb_group_tvshows['IMDb Group'].unique().shape[0]}'\n 
      ''')

      Accross All Platforms Total Count of IMDb Group is '10'

      Total Count of IMDb Group on 'Netflix' is '8'

      Total Count of IMDb Group on 'Hulu' is '9'

      Total Count of IMDb Group on 'Prime Video' is '10'

      Total Count of IMDb Group on 'Disney+' is '7'
 
      

fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_i_ax1 = sns.lineplot(y = imdb_group_data_tvshows['IMDb Group'], x = imdb_group_data_tvshows['Netflix'], color = 'red', ax = axes[0, 0])
h_i_ax2 = sns.lineplot(y = imdb_group_data_tvshows['IMDb Group'], x = imdb_group_data_tvshows['Hulu'], color = 'lightgreen', ax = axes[0, 1])
p_i_ax3 = sns.lineplot(y = imdb_group_data_tvshows['IMDb Group'], x = imdb_group_data_tvshows['Prime Video'], color = 'lightblue', ax = axes[1, 0])
d_i_ax4 = sns.lineplot(y = imdb_group_data_tvshows['IMDb Group'], x = imdb_group_data_tvshows['Disney+'], color = 'darkblue', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_i_ax1.title.set_text(labels[0])
h_i_ax2.title.set_text(labels[1])
p_i_ax3.title.set_text(labels[2])
d_i_ax4.title.set_text(labels[3])

plt.show()

fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_i_ax1 = sns.barplot(x = imdb_group_data_tvshows['IMDb Group'][:10], y = imdb_group_data_tvshows['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_i_ax2 = sns.barplot(x = imdb_group_data_tvshows['IMDb Group'][:10], y = imdb_group_data_tvshows['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_i_ax3 = sns.barplot(x = imdb_group_data_tvshows['IMDb Group'][:10], y = imdb_group_data_tvshows['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_i_ax4 = sns.barplot(x = imdb_group_data_tvshows['IMDb Group'][:10], y = imdb_group_data_tvshows['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_i_ax1.title.set_text(labels[0])
h_i_ax2.title.set_text(labels[1])
p_i_ax3.title.set_text(labels[2])
d_i_ax4.title.set_text(labels[3])
 
plt.show()